{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装第三方库\n",
    "!pip install hmmlearn mplfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np\n",
    "from hmmlearn.hmm import GaussianHMM\n",
    "import pandas as pd\n",
    "from mplfinance.original_flavor import candlestick_ochl\n",
    "from matplotlib.dates import date2num, YearLocator, DateFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close_price</th>\n",
       "      <th>open_price</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>amount</th>\n",
       "      <th>diff_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-28</td>\n",
       "      <td>3038.55</td>\n",
       "      <td>3028.60</td>\n",
       "      <td>3039.69</td>\n",
       "      <td>3020.23</td>\n",
       "      <td>2.689000e+10</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-11-27</td>\n",
       "      <td>3031.70</td>\n",
       "      <td>3038.19</td>\n",
       "      <td>3038.36</td>\n",
       "      <td>3015.01</td>\n",
       "      <td>3.105000e+10</td>\n",
       "      <td>-0.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-11-24</td>\n",
       "      <td>3040.97</td>\n",
       "      <td>3060.33</td>\n",
       "      <td>3060.33</td>\n",
       "      <td>3037.20</td>\n",
       "      <td>2.878000e+10</td>\n",
       "      <td>-0.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-11-23</td>\n",
       "      <td>3061.86</td>\n",
       "      <td>3041.68</td>\n",
       "      <td>3062.87</td>\n",
       "      <td>3034.30</td>\n",
       "      <td>2.766000e+10</td>\n",
       "      <td>0.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-11-22</td>\n",
       "      <td>3043.61</td>\n",
       "      <td>3060.50</td>\n",
       "      <td>3067.96</td>\n",
       "      <td>3043.61</td>\n",
       "      <td>2.891000e+10</td>\n",
       "      <td>-0.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4590</th>\n",
       "      <td>2005-01-10</td>\n",
       "      <td>1252.40</td>\n",
       "      <td>1243.58</td>\n",
       "      <td>1252.72</td>\n",
       "      <td>1236.09</td>\n",
       "      <td>7.235000e+08</td>\n",
       "      <td>0.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4591</th>\n",
       "      <td>2005-01-07</td>\n",
       "      <td>1244.75</td>\n",
       "      <td>1239.32</td>\n",
       "      <td>1256.31</td>\n",
       "      <td>1235.51</td>\n",
       "      <td>8.941000e+08</td>\n",
       "      <td>0.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4592</th>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>1239.43</td>\n",
       "      <td>1252.49</td>\n",
       "      <td>1252.73</td>\n",
       "      <td>1234.24</td>\n",
       "      <td>7.922000e+08</td>\n",
       "      <td>-1.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4593</th>\n",
       "      <td>2005-01-05</td>\n",
       "      <td>1251.94</td>\n",
       "      <td>1241.68</td>\n",
       "      <td>1258.58</td>\n",
       "      <td>1235.75</td>\n",
       "      <td>8.679000e+08</td>\n",
       "      <td>0.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4594</th>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>1242.77</td>\n",
       "      <td>1260.78</td>\n",
       "      <td>1260.78</td>\n",
       "      <td>1238.18</td>\n",
       "      <td>8.162000e+08</td>\n",
       "      <td>-1.87%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4595 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close_price  open_price     high      low        amount  \\\n",
       "0    2023-11-28      3038.55     3028.60  3039.69  3020.23  2.689000e+10   \n",
       "1    2023-11-27      3031.70     3038.19  3038.36  3015.01  3.105000e+10   \n",
       "2    2023-11-24      3040.97     3060.33  3060.33  3037.20  2.878000e+10   \n",
       "3    2023-11-23      3061.86     3041.68  3062.87  3034.30  2.766000e+10   \n",
       "4    2023-11-22      3043.61     3060.50  3067.96  3043.61  2.891000e+10   \n",
       "...         ...          ...         ...      ...      ...           ...   \n",
       "4590 2005-01-10      1252.40     1243.58  1252.72  1236.09  7.235000e+08   \n",
       "4591 2005-01-07      1244.75     1239.32  1256.31  1235.51  8.941000e+08   \n",
       "4592 2005-01-06      1239.43     1252.49  1252.73  1234.24  7.922000e+08   \n",
       "4593 2005-01-05      1251.94     1241.68  1258.58  1235.75  8.679000e+08   \n",
       "4594 2005-01-04      1242.77     1260.78  1260.78  1238.18  8.162000e+08   \n",
       "\n",
       "     diff_ratio  \n",
       "0         0.23%  \n",
       "1        -0.30%  \n",
       "2        -0.68%  \n",
       "3         0.60%  \n",
       "4        -0.79%  \n",
       "...         ...  \n",
       "4590      0.61%  \n",
       "4591      0.43%  \n",
       "4592     -1.00%  \n",
       "4593      0.74%  \n",
       "4594     -1.87%  \n",
       "\n",
       "[4595 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "def convert_amount(x):\n",
    "    if x[-1] == 'B':\n",
    "        return float(x[:-1]) * 1e9\n",
    "    elif x[-1] == 'M':\n",
    "        return float(x[:-1]) * 1e6\n",
    "    elif x[-1] == 'K':\n",
    "        return float(x[:-1]) * 1e3\n",
    "    else:\n",
    "        raise Exception()\n",
    "    \n",
    "data = pd.read_csv('./stock_data.csv', thousands=',')\n",
    "data['amount'] = data['amount'].apply(convert_amount)\n",
    "data['date'] = data['date'].apply(pd.to_datetime)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close_price</th>\n",
       "      <th>open_price</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>amount</th>\n",
       "      <th>diff_ratio</th>\n",
       "      <th>a_5</th>\n",
       "      <th>a_20</th>\n",
       "      <th>r_5</th>\n",
       "      <th>r_20</th>\n",
       "      <th>date2num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-28</td>\n",
       "      <td>3038.55</td>\n",
       "      <td>3028.60</td>\n",
       "      <td>3039.69</td>\n",
       "      <td>3020.23</td>\n",
       "      <td>2.689000e+10</td>\n",
       "      <td>0.23%</td>\n",
       "      <td>-0.206872</td>\n",
       "      <td>-0.173669</td>\n",
       "      <td>-0.009623</td>\n",
       "      <td>0.006531</td>\n",
       "      <td>19689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-11-27</td>\n",
       "      <td>3031.70</td>\n",
       "      <td>3038.19</td>\n",
       "      <td>3038.36</td>\n",
       "      <td>3015.01</td>\n",
       "      <td>3.105000e+10</td>\n",
       "      <td>-0.30%</td>\n",
       "      <td>0.076613</td>\n",
       "      <td>-0.149585</td>\n",
       "      <td>-0.012007</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>19688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-11-24</td>\n",
       "      <td>3040.97</td>\n",
       "      <td>3060.33</td>\n",
       "      <td>3060.33</td>\n",
       "      <td>3037.20</td>\n",
       "      <td>2.878000e+10</td>\n",
       "      <td>-0.68%</td>\n",
       "      <td>0.072772</td>\n",
       "      <td>-0.111664</td>\n",
       "      <td>-0.004397</td>\n",
       "      <td>0.007655</td>\n",
       "      <td>19685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-11-23</td>\n",
       "      <td>3061.86</td>\n",
       "      <td>3041.68</td>\n",
       "      <td>3062.87</td>\n",
       "      <td>3034.30</td>\n",
       "      <td>2.766000e+10</td>\n",
       "      <td>0.60%</td>\n",
       "      <td>0.020085</td>\n",
       "      <td>-0.053496</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.024318</td>\n",
       "      <td>19684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-11-22</td>\n",
       "      <td>3043.61</td>\n",
       "      <td>3060.50</td>\n",
       "      <td>3067.96</td>\n",
       "      <td>3043.61</td>\n",
       "      <td>2.891000e+10</td>\n",
       "      <td>-0.79%</td>\n",
       "      <td>-0.050913</td>\n",
       "      <td>-0.139566</td>\n",
       "      <td>-0.009555</td>\n",
       "      <td>0.023099</td>\n",
       "      <td>19683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4496</th>\n",
       "      <td>2005-06-07</td>\n",
       "      <td>1030.94</td>\n",
       "      <td>1036.62</td>\n",
       "      <td>1055.63</td>\n",
       "      <td>1029.54</td>\n",
       "      <td>1.570000e+09</td>\n",
       "      <td>-0.33%</td>\n",
       "      <td>0.530227</td>\n",
       "      <td>0.173444</td>\n",
       "      <td>-0.028496</td>\n",
       "      <td>-0.096655</td>\n",
       "      <td>12941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4497</th>\n",
       "      <td>2005-06-06</td>\n",
       "      <td>1034.38</td>\n",
       "      <td>1010.38</td>\n",
       "      <td>1034.85</td>\n",
       "      <td>998.23</td>\n",
       "      <td>1.100000e+09</td>\n",
       "      <td>2.05%</td>\n",
       "      <td>0.246133</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>-0.024618</td>\n",
       "      <td>-0.089159</td>\n",
       "      <td>12940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>2005-06-03</td>\n",
       "      <td>1013.64</td>\n",
       "      <td>1013.90</td>\n",
       "      <td>1019.92</td>\n",
       "      <td>1000.52</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>-0.24%</td>\n",
       "      <td>-0.009950</td>\n",
       "      <td>-0.343590</td>\n",
       "      <td>-0.037098</td>\n",
       "      <td>-0.134139</td>\n",
       "      <td>12937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>2005-06-02</td>\n",
       "      <td>1016.06</td>\n",
       "      <td>1036.73</td>\n",
       "      <td>1036.73</td>\n",
       "      <td>1008.75</td>\n",
       "      <td>1.150000e+09</td>\n",
       "      <td>-2.23%</td>\n",
       "      <td>0.191161</td>\n",
       "      <td>-0.414123</td>\n",
       "      <td>-0.041270</td>\n",
       "      <td>-0.140319</td>\n",
       "      <td>12936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>2005-06-01</td>\n",
       "      <td>1039.19</td>\n",
       "      <td>1059.61</td>\n",
       "      <td>1063.52</td>\n",
       "      <td>1035.71</td>\n",
       "      <td>1.010000e+09</td>\n",
       "      <td>-2.03%</td>\n",
       "      <td>0.029337</td>\n",
       "      <td>-0.297534</td>\n",
       "      <td>-0.031215</td>\n",
       "      <td>-0.100242</td>\n",
       "      <td>12935.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4501 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close_price  open_price     high      low        amount  \\\n",
       "0    2023-11-28      3038.55     3028.60  3039.69  3020.23  2.689000e+10   \n",
       "1    2023-11-27      3031.70     3038.19  3038.36  3015.01  3.105000e+10   \n",
       "2    2023-11-24      3040.97     3060.33  3060.33  3037.20  2.878000e+10   \n",
       "3    2023-11-23      3061.86     3041.68  3062.87  3034.30  2.766000e+10   \n",
       "4    2023-11-22      3043.61     3060.50  3067.96  3043.61  2.891000e+10   \n",
       "...         ...          ...         ...      ...      ...           ...   \n",
       "4496 2005-06-07      1030.94     1036.62  1055.63  1029.54  1.570000e+09   \n",
       "4497 2005-06-06      1034.38     1010.38  1034.85   998.23  1.100000e+09   \n",
       "4498 2005-06-03      1013.64     1013.90  1019.92  1000.52  1.000000e+09   \n",
       "4499 2005-06-02      1016.06     1036.73  1036.73  1008.75  1.150000e+09   \n",
       "4500 2005-06-01      1039.19     1059.61  1063.52  1035.71  1.010000e+09   \n",
       "\n",
       "     diff_ratio       a_5      a_20       r_5      r_20  date2num  \n",
       "0         0.23% -0.206872 -0.173669 -0.009623  0.006531   19689.0  \n",
       "1        -0.30%  0.076613 -0.149585 -0.012007  0.003354   19688.0  \n",
       "2        -0.68%  0.072772 -0.111664 -0.004397  0.007655   19685.0  \n",
       "3         0.60%  0.020085 -0.053496  0.003576  0.024318   19684.0  \n",
       "4        -0.79% -0.050913 -0.139566 -0.009555  0.023099   19683.0  \n",
       "...         ...       ...       ...       ...       ...       ...  \n",
       "4496     -0.33%  0.530227  0.173444 -0.028496 -0.096655   12941.0  \n",
       "4497      2.05%  0.246133  0.009132 -0.024618 -0.089159   12940.0  \n",
       "4498     -0.24% -0.009950 -0.343590 -0.037098 -0.134139   12937.0  \n",
       "4499     -2.23%  0.191161 -0.414123 -0.041270 -0.140319   12936.0  \n",
       "4500     -2.03%  0.029337 -0.297534 -0.031215 -0.100242   12935.0  \n",
       "\n",
       "[4501 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行特征提取，得到5日和20日增长率\n",
    "data['a_5'] = np.log(data['amount']).diff(-5)\n",
    "data['a_20'] = np.log(data['amount']).diff(-20)\n",
    "data['r_5'] = np.log(data['close_price']).diff(-5)\n",
    "data['r_20'] = np.log(data['close_price']).diff(-20)\n",
    "data['date2num'] = data['date'].apply(lambda x: date2num(x))\n",
    "data = data[data['date'] >= '2005-06-01']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用隐马尔可夫模型对数据建模\n",
    "cols = ['r_5', 'r_20', 'a_5', 'a_20']\n",
    "model = GaussianHMM(n_components=3, covariance_type='full',\n",
    "                    n_iter=1000, random_state=1024)\n",
    "model.fit(data[cols])\n",
    "hidden_status = model.predict(data[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_data(ax, _data):\n",
    "    '''\n",
    "    使用柱状图表示股市数据\n",
    "    '''\n",
    "    candlestick_ochl(ax, _data[['date2num', 'open_price',\n",
    "                                'close_price', 'high', 'low']].values,\n",
    "                     colorup='r', colordown='g', width=0.5)\n",
    "    ax.xaxis.set_major_locator(YearLocator())\n",
    "    ax.xaxis.set_major_formatter(DateFormatter('%Y'))\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示模型结果\n",
    "fig = plt.figure(figsize=(12, 8), dpi=80)\n",
    "ax0 = fig.add_subplot(4, 1, 1)\n",
    "draw_data(ax0, data)\n",
    "for i in range(max(hidden_status)+1):\n",
    "    _data = data[hidden_status == i]\n",
    "    ax = fig.add_subplot(4, 1, i + 2, sharex=ax0, sharey=ax0)\n",
    "    draw_data(ax, _data)\n",
    "plt.savefig('stock_analysis.png', dpi=200)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
